The use of digital devices to collect and store irradiance patterns representative of the scene has provided a convenient means for storing and processing images. Consequently image processing algorithms are increasingly used to decode the sampled patterns into a form conveying the most relevant information to the user. Examples include but are not limited to using algorithms to remove optical aberrations and extract depth information encoded into the collected irradiance pattern. For improved results from post-processing, the imaging system has evolved into functionally encoded optics (both light collection and illumination) to enhance the information extraction capacity for parameters relevant for the user. Such designs may incorporate the use of aberrations, such as defocus for example, to enhance the encoding of 3D information as the shape/size/position/orientation/rotation of an object.
Previous implementations for realizing 3D in-focus imaging have focused on modification of the optical system point spread function (PSF) such that it is constant with the axial position of the object. Post-processing algorithms are implemented to solve the inverse problem which estimates the original object from the device image and invariant PSF. Such systems have required a trade-off between axial invariance and suppression of the spatial frequency content and hence loss of restoration quality in the MTF. Alternatively, restoration quality can be strongly dependent upon the spatial frequency due to optical encoding MTFs which are not rotationally symmetric for example.
Both classical lenses and the axial invariant solutions implemented are by definition ill-suited for axial ranging or three dimensional imaging and sensing. Axial ranging requires that the optical PSF be designed to change rapidly as a function of axial location of the object. Given an image with a highly axially variant PSF, the 3D position of the object may be efficiently encoded for estimation in the post-processing algorithm. Point spread functions designed for three dimensional imaging have therefore been ill-suited for extended depth of field.
Previous implementations have focused on either particular application despite the need for all information to be collected simultaneously. There is thus a need for optical system designs with matched post-processing algorithms which can generate images without the presence of defocus as well as provide a three dimensional estimation of the scene.
Photo-activation localization microscopy provides far-field super-resolution imaging techniques based on the periodic photoactivation or photoswitching of the fluorescence emission of sparse arrays of molecules, followed by localization with sub-diffraction limit precision that allows the generation of super-resolution images molecule-by-molecule. This group of techniques may be known under the acronyms STORM (Stochastic Optical Reconstruction Microscopy), PALM (Photo-Activated Localization Microscopy), and variants. Several methods for the generation of three-dimensional images have been demonstrated in traditional systems. However, the resolution of these techniques are not as fine as desired.
There is also a significant current interest in decreasing the data acquisition time by engineering new fluorescent proteins and organic fluorophores along with optical improvements in photon collection. Significant efforts are also underway to reduce systematic errors such as vibrations, drift, and imperfections of the detector, as well as asymmetric molecular emission. Nevertheless, the newly discovered frontiers of optical resolution and functionality—such as quantitative tracking and distance measurement—have not been fully realized yet.
There is thus a need for tools and techniques to improve the resolution of photo-activation location microscopy.